Exterior computed tomography image reconstruction based on anisotropic relative total variation in polar coordinates

Abstract

In computed tomography (CT) image reconstruction problems, exterior CT is an important application in industrial non-destructive testing (NDT). Different from the limited-angle problem that misses part of the rotation angle, the rotation angle of the exterior problem is complete, but for each rotation angle, the projection data through the central region of the object cannot be collected, so that the exterior CT problem is ill-posed inverse problem. The results of traditional reconstruction methods like filtered back-projection (FBP) and simultaneous algebra reconstruction technique (SART) have artifacts along the radial direction edges for exterior CT reconstruction. In this study, we propose and test an anisotropic relative total variation in polar coordinates (P-ARTV) model for addressing the exterior CT problem. Since relative total variation (RTV) can effectively distinguish edges from noises, and P-ARTV with different weights in radial and tangential directions can effectively enhance radial edges, a two-step iteration algorithm was developed to solve the P-ARTV model in this study. The fidelity term and the regularization term are solved in Cartesian and polar coordinate systems, respectively. Numerical experiments show that our new model yields better performance than the existing state-of-the-art algorithms.

An image reconstruction algorithm for spectral exterior CT problem based on edge-preserving diffusion and edge-preserving smoothing

“…In 2021, Pan's group developed the Directional-TV (DTV) model [21], which can reconstruct good CT images from projection data corresponding to a very challenging angular range. In 2022, Guo et al extended the idea of relative total variation (RTV) to exterior CT problems and proposed the P-ARTV model [22], which also performs regularization on the polar coordinate system.…”